Eve – Managed OpenClaw for work
AgentEve is an AI agent harness that runs in an isolated Linux sandbox (2 vCPUs, 4GB RAM, 10GB disk) with a real filesystem, headless Chromium, code execution, and connectors to 1000+ services.You give it a task and it works in the background until it's done.I built this because I wanted OpenClaw wi
Capabilities7 decomposed
managed-openai-api-abstraction-layer
Medium confidenceProvides a managed wrapper around OpenAI's API that handles authentication, rate limiting, request queuing, and error recovery without requiring developers to manage API keys directly or implement retry logic. The system likely uses a proxy architecture that intercepts API calls, applies organizational policies, and routes requests through Eve's infrastructure to enforce usage controls and audit trails.
Positions itself as a managed layer specifically for 'OpenClaw' (likely OpenAI) that centralizes authentication and governance at the organizational level rather than requiring per-developer API key management, with built-in cost controls and audit logging
Simpler than building internal API proxy infrastructure and more governance-focused than direct OpenAI API usage, but adds latency compared to direct client-side calls
team-access-control-and-provisioning
Medium confidenceImplements role-based access control (RBAC) and team member provisioning that allows administrators to grant/revoke AI tool access, set usage quotas per user or team, and manage API key distribution without exposing secrets. The system likely uses a permission matrix tied to organizational hierarchy and tracks access through session tokens or OAuth-style delegation.
Combines team provisioning with usage quota enforcement at the organizational level, likely using a centralized permission store that validates every API call against user quotas and team policies before forwarding to the underlying LLM provider
More integrated than managing OpenAI team accounts separately; provides centralized quota enforcement that per-user API keys cannot offer
usage-monitoring-and-cost-analytics
Medium confidenceTracks all API calls made through Eve's managed layer, aggregates metrics by user/team/project, and provides dashboards showing token consumption, cost breakdown, and usage trends. The system likely logs request metadata (prompt length, completion length, model used, timestamp) and computes costs in real-time based on provider pricing, enabling cost attribution and forecasting.
Provides organization-wide cost visibility and attribution that individual OpenAI accounts cannot offer, likely using a metered billing model where Eve captures every call and computes costs server-side rather than relying on OpenAI's usage dashboard
More granular than OpenAI's native team billing; enables cost allocation to specific teams/projects without manual spreadsheet tracking
policy-enforcement-and-usage-guardrails
Medium confidenceEnforces organizational policies on AI usage by intercepting requests and applying rules such as blocking certain model types, enforcing prompt content filters, rate limiting per user, or preventing API calls outside business hours. The system likely uses a policy engine that evaluates each request against a rule set before forwarding to the LLM provider, with configurable actions (allow, deny, log, alert).
Implements server-side policy enforcement that intercepts all API calls before they reach the LLM provider, enabling organization-wide controls that cannot be bypassed by individual developers using direct API keys
More centralized and enforceable than client-side guardrails; prevents policy circumvention that direct API key usage allows
multi-workspace-and-organization-isolation
Medium confidenceSupports multiple isolated organizational workspaces within a single Eve instance, with separate billing, team rosters, policies, and audit logs per workspace. The system likely uses tenant isolation patterns (database row-level security, namespace prefixes, or separate data stores) to ensure data and configuration from one organization cannot leak into another.
Provides true multi-tenant isolation at the organizational level, allowing separate teams/companies to use Eve without visibility into each other's usage, costs, or policies — a feature not available with direct OpenAI API usage
Enables managed AI infrastructure for agencies and enterprises; direct OpenAI accounts lack this organizational isolation capability
api-key-and-credential-management
Medium confidenceCentralizes API key generation, rotation, and revocation for team members, eliminating the need for developers to manage OpenAI credentials directly. The system likely generates short-lived tokens or session keys tied to Eve's authentication layer, with automatic rotation policies and audit trails for key creation/revocation events.
Abstracts away OpenAI API key management entirely, replacing it with Eve-issued credentials that can be rotated, revoked, and audited centrally without exposing the underlying provider keys
More secure than sharing OpenAI API keys directly; enables credential rotation and revocation that static API keys do not support
audit-logging-and-compliance-reporting
Medium confidenceMaintains comprehensive audit logs of all API calls, access events, policy violations, and administrative actions, with structured logging that includes user identity, timestamp, request details, and outcome. The system likely stores logs in a tamper-resistant format and provides compliance-ready reports (e.g., for SOC2, HIPAA audits) with filtering and export capabilities.
Provides organization-wide audit logging that captures every API call and administrative action in a centralized, tamper-resistant log — a capability that direct OpenAI API usage lacks without building custom logging infrastructure
Enables compliance reporting and incident investigation without custom logging infrastructure; OpenAI's native audit logs are limited to account-level actions
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓teams and enterprises managing multiple developers' AI tool access
- ✓organizations requiring compliance and audit trails for AI usage
- ✓companies wanting to centralize LLM costs and prevent key sprawl
- ✓mid-to-large organizations with multiple teams and departments
- ✓companies with compliance requirements (SOC2, HIPAA, etc.)
- ✓teams practicing principle-of-least-privilege access
- ✓finance and operations teams managing AI infrastructure budgets
- ✓engineering leaders tracking cost efficiency of AI-powered features
Known Limitations
- ⚠adds network latency for every API call (proxy hop adds ~50-200ms)
- ⚠depends on Eve's infrastructure availability — no local fallback option
- ⚠likely limited to OpenAI initially; multi-provider support unknown
- ⚠requires organizational account setup and team member provisioning
- ⚠provisioning delays if manual approval workflows are required
- ⚠no fine-grained capability-level permissions (likely team or user level only)
Requirements
Input / Output
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